Abstract
To increase efficacy in traditional classroom courses as well as in Massive Open Online Courses (MOOCs), automated systems supporting the instructor are needed. One important problem is to automatically detect students that are going to do poorly in a course early enough to be able to take remedial actions. This paper proposes an algorithm that predicts the final grade of each student in a class. It issues a prediction for each student individually, when the expected accuracy of the prediction is sufficient. The algorithm learns online what is the optimal prediction and time to issue a prediction based on past history of students' performance in a course. We derive demonstrate the performance of our algorithm on a dataset obtained based on the performance of approximately 700 undergraduate students who have taken an introductory digital signal processing over the past 7 years. Using data obtained from a pilot course, our methodology suggests that it is effective to perform early in-class assessments such as quizzes, which result in timely performance prediction for each student, thereby enabling timely interventions by the instructor (at the student or class level) when necessary.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - IEEE International Conference on Data Mining, ICDM |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 907-912 |
| Number of pages | 6 |
| Volume | 2016-January |
| ISBN (Print) | 9781467395038 |
| DOIs | |
| State | Published - Jan 5 2016 |
| Externally published | Yes |
| Event | 15th IEEE International Conference on Data Mining, ICDM 2015 - Atlantic City, United States Duration: Nov 14 2015 → Nov 17 2015 |
Other
| Other | 15th IEEE International Conference on Data Mining, ICDM 2015 |
|---|---|
| Country | United States |
| City | Atlantic City |
| Period | 11/14/15 → 11/17/15 |
Fingerprint
Keywords
- Data mining
- Digital signal processing education
- Forecasting algorithms
- Grade prediction
- Online learning
ASJC Scopus subject areas
- Engineering(all)
Cite this
Personalized grade prediction : A data mining approach. / Meier, Yannick; Xu, Jie; Atan, Onur; Van Der Schaar, Mihaela.
Proceedings - IEEE International Conference on Data Mining, ICDM. Vol. 2016-January Institute of Electrical and Electronics Engineers Inc., 2016. p. 907-912 7373410.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Personalized grade prediction
T2 - A data mining approach
AU - Meier, Yannick
AU - Xu, Jie
AU - Atan, Onur
AU - Van Der Schaar, Mihaela
PY - 2016/1/5
Y1 - 2016/1/5
N2 - To increase efficacy in traditional classroom courses as well as in Massive Open Online Courses (MOOCs), automated systems supporting the instructor are needed. One important problem is to automatically detect students that are going to do poorly in a course early enough to be able to take remedial actions. This paper proposes an algorithm that predicts the final grade of each student in a class. It issues a prediction for each student individually, when the expected accuracy of the prediction is sufficient. The algorithm learns online what is the optimal prediction and time to issue a prediction based on past history of students' performance in a course. We derive demonstrate the performance of our algorithm on a dataset obtained based on the performance of approximately 700 undergraduate students who have taken an introductory digital signal processing over the past 7 years. Using data obtained from a pilot course, our methodology suggests that it is effective to perform early in-class assessments such as quizzes, which result in timely performance prediction for each student, thereby enabling timely interventions by the instructor (at the student or class level) when necessary.
AB - To increase efficacy in traditional classroom courses as well as in Massive Open Online Courses (MOOCs), automated systems supporting the instructor are needed. One important problem is to automatically detect students that are going to do poorly in a course early enough to be able to take remedial actions. This paper proposes an algorithm that predicts the final grade of each student in a class. It issues a prediction for each student individually, when the expected accuracy of the prediction is sufficient. The algorithm learns online what is the optimal prediction and time to issue a prediction based on past history of students' performance in a course. We derive demonstrate the performance of our algorithm on a dataset obtained based on the performance of approximately 700 undergraduate students who have taken an introductory digital signal processing over the past 7 years. Using data obtained from a pilot course, our methodology suggests that it is effective to perform early in-class assessments such as quizzes, which result in timely performance prediction for each student, thereby enabling timely interventions by the instructor (at the student or class level) when necessary.
KW - Data mining
KW - Digital signal processing education
KW - Forecasting algorithms
KW - Grade prediction
KW - Online learning
UR - http://www.scopus.com/inward/record.url?scp=84963611408&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84963611408&partnerID=8YFLogxK
U2 - 10.1109/ICDM.2015.54
DO - 10.1109/ICDM.2015.54
M3 - Conference contribution
AN - SCOPUS:84963611408
SN - 9781467395038
VL - 2016-January
SP - 907
EP - 912
BT - Proceedings - IEEE International Conference on Data Mining, ICDM
PB - Institute of Electrical and Electronics Engineers Inc.
ER -